Fish Hydroacoustics Project
ETC5543 – Business Analytics Creative Activity
Dulitha Perera
11 October 2025
Why fish hydroacoustics?
Non-invasive
monitoring
Management:
distinguish
LT
vs
SMB
Question:
can the
FRC (45–170 kHz)
classify species?
Outcome:
scalable pipeline for species ID & trends
About the dataset
~
30k
rows ×
302
variables; two species (
LT
,
SMB
)
Processed in
Echoview
Signals:
F45–F170
(Frequency Response Curve)
Context: morphometrics, depth, speed, orientation
Focus today:
frequency data only
for classification
Acoustic fingerprints: the Frequency Response Curve (FRC)
Different echoes (45–170 kHz)
→ the
FRC
Species show
distinct curve shapes
Hypothesis:
FRC alone
can separate LT vs SMB
Figure 1
Which frequencies separate species?
Compare
LT vs SMB
at each frequency (standardised difference)
Peaks indicate
highly discriminative
frequencies
Figure 2
From curves to models
Each fish’s
FRC (45–170 kHz)
summarised
Quantiles
(q20–q100) &
Median
feasts
features capture curve
shape
(ACF/PACF/STL)
H2O AutoML
across GBM / DL / XGBoost
Grouped CV by
fish
, test on
held-out
set
pipeline
raw
1) Raw Sonar
F45–F170 per ping
agg
2) Per-fish Aggregation
Quantiles (q20–q100) / Median
raw->agg
per fish
feats
3) feasts Features
ACF / PACF / STL (shape)
agg->feats
shape descriptors
note1
Grouped CV
by fishNum (60/20/20)
agg->note1
model
4) H2O AutoML
GBM / Deep Learning / XGBoost
feats->model
features → classifier
note2
Thresholds
policy & OOF clamp [0.40–0.70]
model->note2